How does regularization help in machine learning models?

Regularization plays a crucial role in machine learning by constraining the model parameters, which helps to prevent overfitting. When a model learns from a training dataset, it can sometimes capture noise and fluctuations that are specific to that data rather than underlying patterns applicable to new data. This leads to a model that performs well on the training dataset but poorly on unseen data.

Regularization techniques, such as L1 (Lasso) and L2 (Ridge), add a penalty to the loss function based on the magnitude of the model parameters. By doing so, they effectively limit how much the model can adjust its parameters during training. This constraint encourages simpler models that generalize better, as it reduces the risk of fitting to noise and enhances the model's performance on new, unseen data.

This approach serves to balance the trade-off between fitting the training data closely and maintaining the model's ability to generalize, which is a fundamental concept in building robust machine learning models.