Regularization is a technique employed to mitigate overfitting in machine learning models by reducing excessive complexity. Machine learning models often tend to overfit training data, meaning they perform well on the training set but poorly on unseen data. Regularization introduces a penalty term for assigning overly high weights to features, compelling the model to learn a more generalized and balanced representation, thus enhancing its ability to generalize to new data.
