Answer: When the model is overfitting

Regularization techniques like L1 and L2 are primarily used to prevent overfitting in models. Overfitting happens when a model learns the training data too well, including its noise and outliers, which negatively impacts its performance on unseen data. By adding penalty terms to the model's objective function, regularization discourages the learning of overly complex models. L1 regularization promotes sparsity by penalizing the absolute values of the model parameters, while L2 regularization penalizes the square of the parameters. These techniques are particularly beneficial for complex models or when working with small datasets, as these scenarios are more susceptible to overfitting. It's important to note that the application of regularization is not directly related to the dataset's size (large or small) or the presence of categorical data.

Author: LeetQuiz Editorial Team