diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
index ea1b64cda62f9..d2a4e74fc5e4f 100644
--- a/sklearn/tests/test_public_functions.py
+++ b/sklearn/tests/test_public_functions.py
@@ -172,6 +172,7 @@ def _check_function_param_validation(
     "sklearn.model_selection.train_test_split",
     "sklearn.random_projection.johnson_lindenstrauss_min_dim",
     "sklearn.svm.l1_min_c",
+    "sklearn.utils.gen_batches",
 ]
 
 
diff --git a/sklearn/utils/__init__.py b/sklearn/utils/__init__.py
index 923c08d44c6f4..0b3119c0bfa03 100644
--- a/sklearn/utils/__init__.py
+++ b/sklearn/utils/__init__.py
@@ -39,6 +39,7 @@
 )
 from .. import get_config
 from ._bunch import Bunch
+from ._param_validation import validate_params, Interval
 
 
 # Do not deprecate parallel_backend and register_parallel_backend as they are
@@ -725,6 +726,13 @@ def _chunk_generator(gen, chunksize):
             return
 
 
+@validate_params(
+    {
+        "n": [Interval(numbers.Integral, 1, None, closed="left")],
+        "batch_size": [Interval(numbers.Integral, 1, None, closed="left")],
+        "min_batch_size": [Interval(numbers.Integral, 0, None, closed="left")],
+    }
+)
 def gen_batches(n, batch_size, *, min_batch_size=0):
     """Generator to create slices containing `batch_size` elements from 0 to `n`.
 
@@ -762,12 +770,6 @@ def gen_batches(n, batch_size, *, min_batch_size=0):
     >>> list(gen_batches(7, 3, min_batch_size=2))
     [slice(0, 3, None), slice(3, 7, None)]
     """
-    if not isinstance(batch_size, numbers.Integral):
-        raise TypeError(
-            "gen_batches got batch_size=%s, must be an integer" % batch_size
-        )
-    if batch_size <= 0:
-        raise ValueError("gen_batches got batch_size=%s, must be positive" % batch_size)
     start = 0
     for _ in range(int(n // batch_size)):
         end = start + batch_size
diff --git a/sklearn/utils/tests/test_utils.py b/sklearn/utils/tests/test_utils.py
index 848985f267c92..a000394bbee28 100644
--- a/sklearn/utils/tests/test_utils.py
+++ b/sklearn/utils/tests/test_utils.py
@@ -17,7 +17,6 @@
 from sklearn.utils import check_random_state
 from sklearn.utils import _determine_key_type
 from sklearn.utils import deprecated
-from sklearn.utils import gen_batches
 from sklearn.utils import _get_column_indices
 from sklearn.utils import resample
 from sklearn.utils import safe_mask
@@ -56,19 +55,6 @@ def test_make_rng():
         check_random_state("some invalid seed")
 
 
-def test_gen_batches():
-    # Make sure gen_batches errors on invalid batch_size
-
-    assert_array_equal(list(gen_batches(4, 2)), [slice(0, 2, None), slice(2, 4, None)])
-    msg_zero = "gen_batches got batch_size=0, must be positive"
-    with pytest.raises(ValueError, match=msg_zero):
-        next(gen_batches(4, 0))
-
-    msg_float = "gen_batches got batch_size=0.5, must be an integer"
-    with pytest.raises(TypeError, match=msg_float):
-        next(gen_batches(4, 0.5))
-
-
 def test_deprecated():
     # Test whether the deprecated decorator issues appropriate warnings
     # Copied almost verbatim from https://docs.python.org/library/warnings.html