What is regularization in supervised learning?

Regularization penalizes complex models to prevent overfitting.

Regularization adds a penalty term to the loss function. L1 (Lasso) promotes sparsity, while L2 (Ridge) penalizes large weights. It controls model complexity and improves generalization.