6. In Lasso and Ridge regressions, the penalty term lead to "shrinking" the estimated parameters in the model towards 0. This tends to introduce bias while reducing variance. Why can introducing bias while reducing variance potentially lead to better predictions? Does this argument always apply or just apply in some cases? Explain