diff --git a/.circleci/config.yml b/.circleci/config.yml
index 1e5832b37a7f6..e0ec9a85978f2 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -3,7 +3,7 @@ version: 2.1
 jobs:
   lint:
     docker:
-      - image: cimg/python:3.9.18
+      - image: cimg/python:3.10.16
     steps:
       - checkout
       - run:
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e8730b679a5d6..c40c686751020 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,8 +6,7 @@ repos:
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    # Ruff version.
-    rev: v0.5.1
+    rev: v0.11.0
     hooks:
     -   id: ruff
         args: ["--fix", "--output-format=full"]
diff --git a/build_tools/get_comment.py b/build_tools/get_comment.py
index b357c68f23e3e..21b8b9483306d 100644
--- a/build_tools/get_comment.py
+++ b/build_tools/get_comment.py
@@ -117,7 +117,7 @@ def get_message(log_file, repo, pr_number, sha, run_id, details, versions):
         title="`ruff`",
         message=(
             "`ruff` detected issues. Please run "
-            "`ruff check --fix --output-format=full .` locally, fix the remaining "
+            "`ruff check --fix --output-format=full` locally, fix the remaining "
             "issues, and push the changes. Here you can see the detected issues. Note "
             f"that the installed `ruff` version is `ruff={versions['ruff']}`."
         ),
diff --git a/build_tools/linting.sh b/build_tools/linting.sh
index aefabfae7b3f5..5af5709652225 100755
--- a/build_tools/linting.sh
+++ b/build_tools/linting.sh
@@ -23,7 +23,7 @@ else
 fi
 
 echo -e "### Running ruff ###\n"
-ruff check --output-format=full .
+ruff check --output-format=full
 status=$?
 if [[ $status -eq 0 ]]
 then
diff --git a/pyproject.toml b/pyproject.toml
index a96c517cf840e..ace8cab9cb89e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -83,7 +83,7 @@ tests = [
     "pandas>=1.4.0",
     "pytest>=7.1.2",
     "pytest-cov>=2.9.0",
-    "ruff>=0.5.1",
+    "ruff>=0.11.0",
     "black>=24.3.0",
     "mypy>=1.9",
     "pyamg>=5.0.0",
@@ -126,7 +126,6 @@ exclude = '''
 [tool.ruff]
 # max line length for black
 line-length = 88
-target-version = "py38"
 exclude=[
     ".git",
     "__pycache__",
@@ -146,7 +145,7 @@ exclude=[
 preview = true
 # This enables us to use the explicit preview rules that we want only
 explicit-preview-rules = true
-# all rules can be found here: https://beta.ruff.rs/docs/rules/
+# all rules can be found here: https://docs.astral.sh/ruff/rules/
 select = ["E", "F", "W", "I", "CPY001", "RUF"]
 ignore=[
     # space before : (needed for how black formats slicing)
@@ -155,11 +154,11 @@ ignore=[
     "E731",
     # do not use variables named 'l', 'O', or 'I'
     "E741",
-    # E721 is in preview (july 2024) and gives many false positives.
+    # E721 gives many false positives.
     # Use `is` and `is not` for type comparisons, or `isinstance()` for
     # isinstance checks
     "E721",
-    # F841 is in preview (july 2024), and we don't care much about it.
+    # We don't care much about F841.
     # Local variable ... is assigned to but never used
     "F841",
     # some RUF rules trigger too many changes
diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py
index 8e0592abddd74..8ef448ab0c48c 100644
--- a/sklearn/_min_dependencies.py
+++ b/sklearn/_min_dependencies.py
@@ -32,7 +32,7 @@
     "memory_profiler": ("0.57.0", "benchmark, docs"),
     "pytest": (PYTEST_MIN_VERSION, "tests"),
     "pytest-cov": ("2.9.0", "tests"),
-    "ruff": ("0.5.1", "tests"),
+    "ruff": ("0.11.0", "tests"),
     "black": ("24.3.0", "tests"),
     "mypy": ("1.9", "tests"),
     "pyamg": ("5.0.0", "tests"),
diff --git a/sklearn/datasets/tests/test_base.py b/sklearn/datasets/tests/test_base.py
index 8b5231f68abdd..0bf63a7c3483d 100644
--- a/sklearn/datasets/tests/test_base.py
+++ b/sklearn/datasets/tests/test_base.py
@@ -255,7 +255,7 @@ def test_load_diabetes_raw():
     get an unscaled version when setting `scaled=False`."""
     diabetes_raw = load_diabetes(scaled=False)
     assert diabetes_raw.data.shape == (442, 10)
-    assert diabetes_raw.target.size, 442
+    assert diabetes_raw.target.size == 442
     assert len(diabetes_raw.feature_names) == 10
     assert diabetes_raw.DESCR
 
diff --git a/sklearn/datasets/tests/test_samples_generator.py b/sklearn/datasets/tests/test_samples_generator.py
index 5611f8d2d02ac..ad2021dd770ea 100644
--- a/sklearn/datasets/tests/test_samples_generator.py
+++ b/sklearn/datasets/tests/test_samples_generator.py
@@ -112,7 +112,7 @@ def test_make_classification_informative_features():
         (2, [1 / 2] * 2, 2),
         (2, [3 / 4, 1 / 4], 2),
         (10, [1 / 3] * 3, 10),
-        (int(64), [1], 1),
+        (64, [1], 1),
     ]:
         n_classes = len(weights)
         n_clusters = n_classes * n_clusters_per_class
diff --git a/sklearn/decomposition/tests/test_incremental_pca.py b/sklearn/decomposition/tests/test_incremental_pca.py
index e12be7337cbb3..6bca13d0ad627 100644
--- a/sklearn/decomposition/tests/test_incremental_pca.py
+++ b/sklearn/decomposition/tests/test_incremental_pca.py
@@ -1,5 +1,6 @@
 """Tests for Incremental PCA."""
 
+import itertools
 import warnings
 
 import numpy as np
@@ -228,7 +229,7 @@ def test_incremental_pca_batch_signs():
         ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X)
         all_components.append(ipca.components_)
 
-    for i, j in zip(all_components[:-1], all_components[1:]):
+    for i, j in itertools.pairwise(all_components):
         assert_almost_equal(np.sign(i), np.sign(j), decimal=6)
 
 
@@ -265,7 +266,7 @@ def test_incremental_pca_batch_values():
         ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X)
         all_components.append(ipca.components_)
 
-    for i, j in zip(all_components[:-1], all_components[1:]):
+    for i, j in itertools.pairwise(all_components):
         assert_almost_equal(i, j, decimal=1)
 
 
@@ -281,7 +282,7 @@ def test_incremental_pca_batch_rank():
         ipca = IncrementalPCA(n_components=20, batch_size=batch_size).fit(X)
         all_components.append(ipca.components_)
 
-    for components_i, components_j in zip(all_components[:-1], all_components[1:]):
+    for components_i, components_j in itertools.pairwise(all_components):
         assert_allclose_dense_sparse(components_i, components_j)
 
 
@@ -300,7 +301,7 @@ def test_incremental_pca_partial_fit():
     pipca = IncrementalPCA(n_components=2, batch_size=batch_size)
     # Add one to make sure endpoint is included
     batch_itr = np.arange(0, n + 1, batch_size)
-    for i, j in zip(batch_itr[:-1], batch_itr[1:]):
+    for i, j in itertools.pairwise(batch_itr):
         pipca.partial_fit(X[i:j, :])
     assert_almost_equal(ipca.components_, pipca.components_, decimal=3)
 
diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py
index aadf230fd751e..fcefa31db097c 100644
--- a/sklearn/ensemble/tests/test_forest.py
+++ b/sklearn/ensemble/tests/test_forest.py
@@ -926,7 +926,7 @@ def test_parallel_train():
 
     X_test = rng.randn(n_samples, n_features)
     probas = [clf.predict_proba(X_test) for clf in clfs]
-    for proba1, proba2 in zip(probas, probas[1:]):
+    for proba1, proba2 in itertools.pairwise(probas):
         assert_array_almost_equal(proba1, proba2)
 
 
diff --git a/sklearn/manifold/tests/test_spectral_embedding.py b/sklearn/manifold/tests/test_spectral_embedding.py
index d63f6bd33fc96..7826fe64eede2 100644
--- a/sklearn/manifold/tests/test_spectral_embedding.py
+++ b/sklearn/manifold/tests/test_spectral_embedding.py
@@ -1,3 +1,4 @@
+import itertools
 from unittest.mock import Mock
 
 import numpy as np
@@ -71,7 +72,7 @@ def test_sparse_graph_connected_component(coo_container):
     p = rng.permutation(n_samples)
     connections = []
 
-    for start, stop in zip(boundaries[:-1], boundaries[1:]):
+    for start, stop in itertools.pairwise(boundaries):
         group = p[start:stop]
         # Connect all elements within the group at least once via an
         # arbitrary path that spans the group.
@@ -91,7 +92,7 @@ def test_sparse_graph_connected_component(coo_container):
     affinity = coo_container((data, (row_idx, column_idx)))
     affinity = 0.5 * (affinity + affinity.T)
 
-    for start, stop in zip(boundaries[:-1], boundaries[1:]):
+    for start, stop in itertools.pairwise(boundaries):
         component_1 = _graph_connected_component(affinity, p[start])
         component_size = stop - start
         assert component_1.sum() == component_size
diff --git a/sklearn/model_selection/tests/test_split.py b/sklearn/model_selection/tests/test_split.py
index f26c9bd2b34ff..c7af88ad2666b 100644
--- a/sklearn/model_selection/tests/test_split.py
+++ b/sklearn/model_selection/tests/test_split.py
@@ -756,7 +756,7 @@ def test_shuffle_split():
     ss1 = ShuffleSplit(test_size=0.2, random_state=0).split(X)
     ss2 = ShuffleSplit(test_size=2, random_state=0).split(X)
     ss3 = ShuffleSplit(test_size=np.int32(2), random_state=0).split(X)
-    ss4 = ShuffleSplit(test_size=int(2), random_state=0).split(X)
+    ss4 = ShuffleSplit(test_size=2, random_state=0).split(X)
     for t1, t2, t3, t4 in zip(ss1, ss2, ss3, ss4):
         assert_array_equal(t1[0], t2[0])
         assert_array_equal(t2[0], t3[0])
diff --git a/sklearn/neural_network/_multilayer_perceptron.py b/sklearn/neural_network/_multilayer_perceptron.py
index 6c09ca4f804e4..b223a4173120d 100644
--- a/sklearn/neural_network/_multilayer_perceptron.py
+++ b/sklearn/neural_network/_multilayer_perceptron.py
@@ -5,7 +5,7 @@
 
 import warnings
 from abc import ABC, abstractmethod
-from itertools import chain
+from itertools import chain, pairwise
 from numbers import Integral, Real
 
 import numpy as np
@@ -491,7 +491,7 @@ def _fit(self, X, y, sample_weight=None, incremental=False):
 
         coef_grads = [
             np.empty((n_fan_in_, n_fan_out_), dtype=X.dtype)
-            for n_fan_in_, n_fan_out_ in zip(layer_units[:-1], layer_units[1:])
+            for n_fan_in_, n_fan_out_ in pairwise(layer_units)
         ]
 
         intercept_grads = [
diff --git a/sklearn/tree/_classes.py b/sklearn/tree/_classes.py
index 53a1187ec5a50..870d7313b4fb9 100644
--- a/sklearn/tree/_classes.py
+++ b/sklearn/tree/_classes.py
@@ -322,12 +322,12 @@ def _fit(
         if isinstance(self.min_samples_leaf, numbers.Integral):
             min_samples_leaf = self.min_samples_leaf
         else:  # float
-            min_samples_leaf = int(ceil(self.min_samples_leaf * n_samples))
+            min_samples_leaf = ceil(self.min_samples_leaf * n_samples)
 
         if isinstance(self.min_samples_split, numbers.Integral):
             min_samples_split = self.min_samples_split
         else:  # float
-            min_samples_split = int(ceil(self.min_samples_split * n_samples))
+            min_samples_split = ceil(self.min_samples_split * n_samples)
             min_samples_split = max(2, min_samples_split)
 
         min_samples_split = max(min_samples_split, 2 * min_samples_leaf)
diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py
index ade052cbeebcc..8348cd29e1c8e 100644
--- a/sklearn/tree/tests/test_tree.py
+++ b/sklearn/tree/tests/test_tree.py
@@ -8,7 +8,7 @@
 import pickle
 import re
 import struct
-from itertools import chain, product
+from itertools import chain, pairwise, product
 
 import joblib
 import numpy as np
@@ -1865,7 +1865,7 @@ def assert_pruning_creates_subtree(estimator_cls, X, y, pruning_path):
 
     # A pruned tree must be a subtree of the previous tree (which had a
     # smaller ccp_alpha)
-    for prev_est, next_est in zip(estimators, estimators[1:]):
+    for prev_est, next_est in pairwise(estimators):
         assert_is_subtree(prev_est.tree_, next_est.tree_)
 
 
diff --git a/sklearn/utils/_tags.py b/sklearn/utils/_tags.py
index c8b1623682a0c..4843a7b0035c5 100644
--- a/sklearn/utils/_tags.py
+++ b/sklearn/utils/_tags.py
@@ -3,7 +3,7 @@
 import warnings
 from collections import OrderedDict
 from dataclasses import dataclass, field
-from itertools import chain
+from itertools import chain, pairwise
 
 from .fixes import _dataclass_args
 
@@ -437,7 +437,7 @@ def get_tags(estimator) -> Tags:
         # inheritance
         sklearn_tags_diff = {}
         items = list(sklearn_tags_provider.items())
-        for current_item, next_item in zip(items[:-1], items[1:]):
+        for current_item, next_item in pairwise(items):
             current_name, current_tags = current_item
             next_name, next_tags = next_item
             current_tags = _to_old_tags(current_tags)
diff --git a/sklearn/utils/sparsefuncs.py b/sklearn/utils/sparsefuncs.py
index fb29de8ad7c6e..a9f2c14035b80 100644
--- a/sklearn/utils/sparsefuncs.py
+++ b/sklearn/utils/sparsefuncs.py
@@ -3,6 +3,8 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
+import itertools
+
 import numpy as np
 import scipy.sparse as sp
 from scipy.sparse.linalg import LinearOperator
@@ -704,7 +706,7 @@ def csc_median_axis_0(X):
     n_samples, n_features = X.shape
     median = np.zeros(n_features)
 
-    for f_ind, (start, end) in enumerate(zip(indptr[:-1], indptr[1:])):
+    for f_ind, (start, end) in enumerate(itertools.pairwise(indptr)):
         # Prevent modifying X in place
         data = np.copy(X.data[start:end])
         nz = n_samples - data.size
diff --git a/sklearn/utils/tests/test_extmath.py b/sklearn/utils/tests/test_extmath.py
index 67851b62ea0ba..74cb47388692f 100644
--- a/sklearn/utils/tests/test_extmath.py
+++ b/sklearn/utils/tests/test_extmath.py
@@ -1,6 +1,8 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
+import itertools
+
 import numpy as np
 import pytest
 from scipy import linalg, sparse
@@ -905,7 +907,7 @@ def test_incremental_variance_ddof():
         if steps[-1] != X.shape[0]:
             steps = np.hstack([steps, n_samples])
 
-        for i, j in zip(steps[:-1], steps[1:]):
+        for i, j in itertools.pairwise(steps):
             batch = X[i:j, :]
             if i == 0:
                 incremental_means = batch.mean(axis=0)