Which Regularizer Should You Actually Use? Lessons from 134,400 Simulations

When optimizing for predictive accuracy, the choice between Ridge, Lasso, and ElasticNet regularization has a negligible impact on performance. Given their near-equal accuracy, you should default to Ridge regression as it is dramatically faster to compute. However, the decision becomes critical for variable selection, where Lasso's ability to identify true features collapses when data has high multicollinearity. In these common scenarios, ElasticNet is the safest and most robust choice, reliably identifying important features even when they are highly correlated.