Why Use Adjusted R-Squared?

You are fitting a linear regression and your colleague suggests adding more predictors to improve the model. They point out that $R^2$ keeps increasing as you add variables. What is wrong with using $R^2$ as the sole criterion for model selection, and how does adjusted $R^2$ address this?