R-squared shows how well the data fit the regression model (the goodness of fit). R-squared can take any value between 0 to 1.

R-Squared is also termed the standardized version of MSE. R-squared **represents the fraction of variance of the actual value of the response variable captured by the regression model** rather than the MSE which captures the residual error.

`R-Squared = 1 – (SSE/SST)`

`R-Squared = SSR/SST`

It is recommended to use R-Squared or rather adjusted R-Squared for evaluating the model performance of the regression models. This is primarily because R-Squared captures the fraction of variance of actual values captured by the regression model and tends to give a better picture of the quality of the regression model. Also, MSE values differ based on whether the values of the response variable are scaled or not. **A better measure instead of MSE is the root mean squared error (RMSE)** which takes care of the fact related to whether the values of the response variable are scaled or not.