Feature scaling is an integral part of preprocessing our dataset before we delve into the machine learning model and mind this, it can create a world of difference.

It speeds up gradient descent by making it require fewer iterations to get to a good solution. Feature scaling speeds up gradient descent by avoiding many extra iterations that are required when one or more features take on much larger values than the rest.

This is the main reason for using feature scaling.