Regularization algorithms are techniques used during model training to discourage complex models and prevent overfitting by adding some form of penalty or constraint. In practice, this often means modifying the learning objective: for example, adding a term to the loss function that increases when model weights become large or when the model fits the training data too closely​.Common regularization methods include L1 regularization (lasso), L2 regularization (ridge), and Elastic Net, which combine L1 and L2​. These introduce a penalty equal to either the absolute sum of weights (L1) or sum of squared weights (L2) into the loss – as a result, the model is encouraged to keep weights small, which often yields simpler models that generalize better. L1 tends to drive many weights to zero, effectively performing feature selection, while L2 tends to shrink weights gradually. Other regularization algorithms and strategies: Dropout (randomly dropping units during training in neural networks), Early Stopping (halting training when validation performance stops improving, to avoid overfitting the training set), Batch Normalization (which has a side-effect of some regularization), and data augmentation (though not an algorithm per se, it’s a regularization strategy as it broadens the training distribution). There are also regularized model variants like Regularization in decision trees (using reduced error pruning or depth limits) and Convolutional network regularization (like weight decay, which is essentially L2). All these methods share the goal of keeping the model from memorizing noise in the training data by either penalizing complexity or introducing randomness. Thus, “regularization algorithms” refers to this family of approaches that tame model complexity: e.g., Lasso regression adds an L1 penalty, Ridge regression adds L2 penalty, Elastic Net combines both, and these are classical algorithms ensuring the model remains generalizable.​