What technique is used to prevent a model from becoming too complex and potentially overfitting the training data?

Regularization is a fundamental technique employed to prevent a model from becoming overly complex and therefore overfitting the training data. In machine learning, overfitting occurs when a model learns not only the underlying patterns but also the noise in the training data, which leads to poor generalization on new, unseen data.

Regularization works by adding a penalty term to the loss function used for training the model. This penalty discourages the model from fitting the training data too closely by either constraining the size of the coefficients in linear models or limiting the complexity of the model in other ways. Common forms of regularization include L1 (Lasso) and L2 (Ridge) methods, each having its own way of imposing these constraints.

In contrast, feature selection refers to the process of selecting a subset of relevant features for model training, which does not directly address the complexity of the model itself. Validation is the process of assessing a model's performance on a separate dataset to ensure it has not overfitted the training set, but it does not prevent overfitting during the training phase. Normalization involves scaling features to a similar range but doesn’t directly mitigate the risk of a model becoming too complex.

Thus, regularization is specifically designed to regulate