Adjusted R-squared is adjusted specifically for a) the number of independent i.e., predictor variables and the sample size. This statistic provides a more accurate measure of how well a regression model fits the data compared to R-squared, especially when multiple predictors are used.
While R-squared always increases with the addition of more predictors, adjusted R-squared compensates for this by accounting for the number of predictors in the model. It only increases when the new variable improves the model more than would be expected by chance. This adjustment is crucial in order to avoid overfitting, which can occur when too many predictors are included without significant contribution to the model.