Regularization is a technique used in machine learning to prevent overfitting and control the complexity of a model. Overfitting occurs when a model is overly complex and fits the training data extremely well, but fails to generalize to new, unseen data. Regularization helps in preventing overfitting by adding a penalty term to the model's loss function. This penalty term discourages the model from becoming too complex and encourages it to generalize well.

  There are different types of regularization techniques, such as L1 regularization (also known as Lasso regularization) and L2 regularization (also known as Ridge regularization). Both techniques add a regularization term to the loss function, which is a function of the model's parameters.

  L1 regularization adds the absolute values of the parameters multiplied by a hyperparameter λ (lambda) to the loss function. This causes the model to shrink some of the parameter values towards zero, effectively eliminating some features and generating a sparse model. By reducing the number of features, L1 regularization helps control model complexity.

  L2 regularization adds the squared values of the parameters multiplied by a hyperparameter λ to the loss function. This penalty term encourages the model's parameters to be small, but not necessarily zero. As a result, L2 regularization helps control the magnitudes of the parameters and reduces the impact of individual features, hence controlling model complexity.

  In both cases, the hyperparameter λ determines the strength of regularization. A larger λ value will increase the regularization strength, leading to simpler models with smaller parameter values.

  By incorporating regularization, machine learning models are forced to balance between fitting the training data well and avoiding excessive complexity. Regularization helps in finding the optimal trade-off between bias and variance, thereby improving the model's ability to generalize to unseen data.