If you have the Statistics Toolbox, I suggest fitnlm. It produces a number of useful statistics. To the best of my knowledge, these aren’t available with lsqcurvefit and the Optimization Toolbox. As a general rule, if all your functions produce similar residual errors, the model with the fewest parameters to estimate will have the best goodness-of-fit because it has the greatest degrees-of-freedom. The F-statistic may be the best measure, although the likelihood ratio may be best for for comparing two models with the same number of parameters.
That said, there are a number of other techniques to assess regression models. It is too broad a subject to go into here.
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