L1 regularization, also known as Lasso, adds a penalty equal to the absolute value of the magnitude of coefficients to the loss function, promoting sparsity by driving some coefficients to zero. This technique is especially useful for feature selection in high-dimensional datasets, as it can effectively reduce the number of features by eliminating less important ones.