diff --git a/examples/model_selection/plot_learning_curve.py b/examples/model_selection/plot_learning_curve.py
index a7045a0bf6c88..6df16956e6937 100644
--- a/examples/model_selection/plot_learning_curve.py
+++ b/examples/model_selection/plot_learning_curve.py
@@ -179,9 +179,9 @@ def plot_learning_curve(
 X, y = load_digits(return_X_y=True)
 
 title = "Learning Curves (Naive Bayes)"
-# Cross validation with 100 iterations to get smoother mean test and train
+# Cross validation with 50 iterations to get smoother mean test and train
 # score curves, each time with 20% data randomly selected as a validation set.
-cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)
+cv = ShuffleSplit(n_splits=50, test_size=0.2, random_state=0)
 
 estimator = GaussianNB()
 plot_learning_curve(
@@ -190,7 +190,7 @@ def plot_learning_curve(
 
 title = r"Learning Curves (SVM, RBF kernel, $\gamma=0.001$)"
 # SVC is more expensive so we do a lower number of CV iterations:
-cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
+cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
 estimator = SVC(gamma=0.001)
 plot_learning_curve(
     estimator, title, X, y, axes=axes[:, 1], ylim=(0.7, 1.01), cv=cv, n_jobs=4