After selecting a model that you feel is appropriate for use with your data (also known as determining that the approach is the best fit), you need to validate your selection, that is, determine its fit.
A well-fitting regression model results in predicted values close to the observed data values.
The mean model (which uses the mean for every predicted value) would generally be used if there were no informative predictor variables. The fit of a proposed regression model should, therefore, be better than the fit of the mean model.
As a data scientist, you will need to scrutinize the coefficients of determination, measure the standard error of estimate, analyze the significance of regression parameters and confidence intervals ...