A best-fit model, or fitted model, has just the right number of predictors needed to explain the data well. Fitting a model is a trade-off between parsimony and explanatory power (i.e., goodness of fit), because high parsimony models (i.e., models with few parameters) tend to produce a worse fit to the data than low parsimony models.
