cross-validation is implemented in scikit-learn using the cross_val_score function from the model_selection module the parameters of the cross_val_score function are the model we want to evaluatethe training dataand the ground-truth labels let' evaluate logisticregression on the iris datasetin[ ]from sklearn model_selection import cross_val_score from sklearn datasets import load_iris from sklearn linear_model import logisticregression iris load_iris(logreg logisticregression(scores cross_val_score(logregiris datairis targetprint("cross-validation scores{}format(scores)out[ ]cross-validation scores by defaultcross_val_score performs three-fold cross-validationreturning three accuracy values we can change the number of folds used by changing the cv parameterin[ ]scores cross_val_score(logregiris datairis targetcv= print("cross-validation scores{}format(scores)out[ ]cross-validation scores common way to summarize the cross-validation accuracy is to compute the meanin[ ]print("average cross-validation score{ }format(scores mean())out[ ]average cross-validation score using the mean cross-validation we can conclude that we expect the model to be around accurate on average looking at all five scores produced by the five-fold cross-validationwe can also conclude that there is relatively high variance in the accuracy between foldsranging from accuracy to accuracy this could imply that the model is very dependent on the particular folds used for trainingbut it could also just be consequence of the small size of the dataset cross-validation