Ridge regression adds an L2 penalty, proportional to the sum of the squares of the coefficients. It pulls them toward zero without eliminating them, which stabilizes the model in the presence of correlated variables. The larger alpha is, the stronger the regularization.
Example
Ridge(alpha=1.0).fit(X_train_s, y_train)