The L1 norm, also known as the Manhattan norm or taxicab norm, is a measure of vector magnitude calculated as the sum of the absolute values of its components. It is widely used in optimization problems and machine learning for its ability to promote sparsity in solutions, making it a popular choice in regularization techniques like Lasso regression.