What is the primary goal of using Regularization in machine learning?

The primary goal of using regularization in machine learning is to decrease overfitting. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise, leading it to perform poorly on unseen data. Regularization techniques, such as L1 (Lasso) and L2 (Ridge) regularization, add a penalty to the loss function based on the magnitude of the model parameters. This penalization discourages overly complex models and promotes simpler models that generalize better to new data.

By limiting the values of the model parameters, regularization helps ensure that the model retains its ability to learn from the data without becoming too tailored to the training set. Consequently, regularization effectively balances the trade-off between fitting the training data well and maintaining the model's ability to generalize, thus addressing overfitting directly.