Root Mean Squared Error (RMSE) is a standard way to measure the error of a model in predicting quantitative data, representing the square root of the average of the squared differences between predicted and observed values. It provides a single measure of predictive accuracy that is sensitive to large errors and is especially useful for comparing the performance of different models on the same dataset.