diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
index 8c4874edf84c1..6c75998ba6e59 100644
--- a/doc/modules/model_evaluation.rst
+++ b/doc/modules/model_evaluation.rst
@@ -98,9 +98,9 @@ Usage examples:
     >>> from sklearn.model_selection import cross_val_score
     >>> iris = datasets.load_iris()
     >>> X, y = iris.data, iris.target
-    >>> clf = svm.SVC(gamma='scale', probability=True, random_state=0)
-    >>> cross_val_score(clf, X, y, scoring='neg_log_loss') # doctest: +ELLIPSIS
-    array([-0.10..., -0.16..., -0.07...])
+    >>> clf = svm.SVC(gamma='scale', random_state=0)
+    >>> cross_val_score(clf, X, y, scoring='recall_macro') # doctest: +ELLIPSIS
+    array([0.980..., 0.960..., 0.979...])
     >>> model = svm.SVC()
     >>> cross_val_score(model, X, y, scoring='wrong_choice')
     Traceback (most recent call last):
diff --git a/sklearn/svm/classes.py b/sklearn/svm/classes.py
index db9360e64fb4f..99d07b7d51fce 100644
--- a/sklearn/svm/classes.py
+++ b/sklearn/svm/classes.py
@@ -116,16 +116,15 @@ class LinearSVC(BaseEstimator, LinearClassifierMixin,
     >>> from sklearn.svm import LinearSVC
     >>> from sklearn.datasets import make_classification
     >>> X, y = make_classification(n_features=4, random_state=0)
-    >>> clf = LinearSVC(random_state=0)
+    >>> clf = LinearSVC(random_state=0, tol=1e-5)
     >>> clf.fit(X, y)
     LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
          intercept_scaling=1, loss='squared_hinge', max_iter=1000,
-         multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,
-         verbose=0)
+         multi_class='ovr', penalty='l2', random_state=0, tol=1e-05, verbose=0)
     >>> print(clf.coef_)
-    [[0.08551385 0.39414796 0.49847831 0.37513797]]
+    [[0.085... 0.394... 0.498... 0.375...]]
     >>> print(clf.intercept_)
-    [0.28418066]
+    [0.284...]
     >>> print(clf.predict([[0, 0, 0, 0]]))
     [1]
 
@@ -327,17 +326,17 @@ class LinearSVR(LinearModel, RegressorMixin):
     >>> from sklearn.svm import LinearSVR
     >>> from sklearn.datasets import make_regression
     >>> X, y = make_regression(n_features=4, random_state=0)
-    >>> regr = LinearSVR(random_state=0)
+    >>> regr = LinearSVR(random_state=0, tol=1e-5)
     >>> regr.fit(X, y)
     LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,
          intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,
-         random_state=0, tol=0.0001, verbose=0)
+         random_state=0, tol=1e-05, verbose=0)
     >>> print(regr.coef_)
-    [16.35750999 26.91499923 42.30652207 60.47843124]
+    [16.35... 26.91... 42.30... 60.47...]
     >>> print(regr.intercept_)
-    [-4.29756543]
+    [-4.29...]
     >>> print(regr.predict([[0, 0, 0, 0]]))
-    [-4.29756543]
+    [-4.29...]
 
     See also
     --------