Lasso regression adds an L1 penalty, proportional to the sum of the absolute values of the coefficients. It can set some coefficients to zero and therefore acts as variable selection, useful for obtaining a parsimonious model. The larger alpha is, the more coefficients drop to zero.
Example
Lasso(alpha=0.1).fit(X_train_s, y_train)