This sheds light on the obvious disadvantage of ridge regression, which is model interpretability. It will shrink the coefficients for least important predictors, very close to zero. But it will never make them exactly zero. In other words, the final model will include all predictors. However, in the case of the lasso, the L1 penalty has the eﬀect of forcing some of the coeﬃcient estimates to be exactly equal to zero when the tuning parameter λ is suﬃciently large. Therefore, the lasso method also performs variable selection and is said to yield sparse models.