Example Concept: Regularization techniques such as L1 (Lasso) and L2 (Ridge) add a penalty to the loss function to discourage overly complex models. Cross-validation helps in selecting the best model by evaluating its performance on different subsets of the data. Additionally, techniques like dropout in neural networks randomly deactivate neurons during training to prevent co-adaptation of features, thus enhancing generalization.