With using stepwise regression you could actually end up with an overfitting model so I wouldn't consider proving that it fits well your main problem. Stepwise regression is known as one that potentially gives biased results, is discouraged by many authors and even considered a "statistical sin".
With presenting your model you should describe why do you consider it being the best one - saying "because AIC said so" is not enough. Automatic model selection could lead you to choosing a poor model that "fits well" your data but is hard to interpret and/or has a poor predictive power. So there has to be some rationale behind your model so to say why this one is the best by your criteria, and what were the criteria. What is practical significance of your model?
You also seem to ask what else model diagnostic results should you present with describing your model choice. Actually there are at least few more things that could be done - you can read more on them here.
What you should present is model diagnostics, the procedure for model selection, and model selection criteria you used. If your main objective is to show that model fits the data well, then look at the residuals.