Regularization is a way of making a machine learning model more accurate. It does this by adding extra information to the model. This extra information helps the model better understand patterns in data and make better predictions. For example, if a model is predicting the price of a house, regularization can help it take into account all the different factors that could affect the price, like the size of the house, the location, and the age. This helps the model make more accurate predictions.