Example Concept: Regularization techniques like L1 (Lasso) and L2 (Ridge) add a penalty to the loss function to discourage overly complex models, which helps in reducing overfitting. Cross-validation, especially k-fold cross-validation, ensures that the model's performance is consistent across different subsets of the data, providing a more reliable estimate of its generalization ability. Additionally, feature selection methods can be employed to remove irrelevant or redundant features, simplifying the model and reducing the risk of overfitting.