Which statement best describes how lasso influences model sparsity?

• A. It increases the number of predictors as the penalty grows.
• B. It always retains all predictors.
• C. It never reduces the number of predictors.
• D. It tends to produce models with fewer predictors, potentially identifying the most important predictors.

Answer: (D)