L1 regularization, also known as Lasso regularization, is a technique used in machine learning to prevent overfitting. It adds a penalty equal to the absolute value of the coefficients to the loss function. This encourages the model to keep only the most important features, effectively reducing the number of variables it uses. As a result, it can lead to simpler and more interpretable models. By shrinking some coefficients to zero, L1 regularization helps in feature selection, making it easier to understand which inputs are significant. This is particularly useful in high-dimensional datasets, where many features may be irrelevant or redundant.