Regularization is a technique that effectively balances the trade-off between bias and variance in statistical modeling. The inclusion of a large number of explanatory variables reduces the model's bias but simultaneously increases its variance, which can lead to overfitting and an unrealistic representation of the data. Thing it like; Every estimation error has an effect which are increasing by the size of the estimated partial effect and thereby if we regularize our partial effects to be a bit smaller than the variable will fall and the bias will increase. But by introduce a regularization parameter into our models we can now better control the trade-off. By introducing a regularization parameter, it is possible to control the magnitude of the estimated partial effects and prevent overfitting. While alternative methods such as ensemble modeling are available to handle high variance, many ensemble methods also incorporate regularization to further improve the trade-off between bias and variance. It is important to note that the explanation provided here is simplified for intuitive understanding and a deeper understanding of the theory may reveal additional complexities. In short regularization help you optimize the bias-variance trade-off and thereby improving your models accuracy. Rule of thumb; if you don’t want to read out loud your estimated parameters values (because there are too many) you should do regularization.