Models that memorize training data fail to generalize; Elastic Net is introduced as the fix.

Ridge shrinks all coefficients while keeping features; Lasso drives some coefficients exactly to zero.

Elastic Net merges L1 selection and L2 shrinkage for accurate, stable generalization.

Prediction error plus L1 and L2 penalties are minimized together during optimization.

Alpha balances Ridge and Lasso; Lambda sets overall regularization strength.

Especially strong with correlated features across healthcare, finance, marketing, and real estate.

Balance feature selection with coefficient shrinkage for simpler, more robust models.