MLR: R2 and RMSE in assessing quality

How would one judge the quality of a multiple linear regression model if the model had a high value for cross-validated R2 but also a high value for RMSE? How much, or how little, faith should one have that the model could be used to make reliable predictions on future observations? Thanks, in advance, for your response.


TS Contributor
The RMSE probably tells you more about the errors on prediction.

Even though a model may have a high R^2, it's really only one side of the story, and it boils down to how well it predicts actual values.

The standard error of prediction is probably the best measure of the model.