R-Squared is used to access the prediction performance of machine learning model which is calculated by 1 minus the ratio between SSR (sum of squared regression) and SST (sum of squared total). Where SSR is the sum of squared error between the predicted value and the actual value while SST is the sum of squared error between the average value and the actual value. The ideal case is to have SSR equals zero which will make the value of R-squared become 1, the closer the R-squared is closer to 1 the better. Theoretically, the value of R-Squared will never decrease no matter how many irrelevant feature(s) are added to the model. This is because, even though the feature may be irrelevant, there might still have some random correlation with the target variable that might help the prediction a little bit. This can result in the increase in value of R-Squared.