Model selection using cross-validation

We can make use of cross-validation to find out which model is performing better by using the following code:

knn = KNeighborsClassifier(n_neighbors=20)
print(cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean())

The 10-fold cross-validation is as follows:

# 10-fold cross-validation with logistic regression
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
print(cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean())