Niraj Kumar explains how two regression techniques work:
Lasso Regression is a regularization technique used for feature selection using a Shrinkage method also referred to as the penalized regression method.
Lasso is short for Least Absolute Shrinkage and Selection Operator, which uses both for regularization and model selection.
If a model uses the L1 regularization technique, then known as lasso regression.
Click through for a summary of the two techniques.