In Machine Learning, Regularization is a process of adding an additional penalty term in the error function for tuning the function. The additional penalty term in the error function controls the excessively fluctuating function and avoid the coefficients take extreme values. In short, Regularization is used to solve an ill-posed problem or to prevent overfitting.
To understand more about Regularization, check out this Machine Learning Course by Intellipaat.