A data scientist is training a linear model on a wide tabular dataset with roughly 800 numeric features, many of which are believed to be irrelevant or redundant. The team wants a single training approach that both reduces overfitting and automatically drives coefficients of unimportant features to exactly zero, effectively performing feature selection during training. Which regularization approach should they apply?