How do I assess which is the most suitable model?

Hey everyone,

I am making an R project, and I have computed the models using all the methods we've studied in the lectures (particularly Lasso and Forward stepwise selection).
In the conclusion, I have to assess which is the most suitable specification model.

Could you help me writing about 10 lines, emphasizing which are the reasons that brought to me to choose one model over the others?

Thank you. :)


Less is more. Stay pure. Stay poor.
Sounds fun. Did you all use validation methods as well (e.g., bootstrap, validation data spits, k-fold CV, LOOCV)? Did you cover parsimony and minimizing error?
No, I had to compute Lasso, Ridge, stepwise selection and best subset selection on R, without going deeper.
I've done it, now I have to write 10 lines about how do I select one model over the others, underlining the reasons that brought me to make that decision.

It's a statistics course for beginners. Also, English is not my mother language so that's why I'm asking for help in writing that. :)


Less is more. Stay pure. Stay poor.
It all depends on what you covered in classes for benefits. Typically lasso is preferred if there were close to as many predictors as observations. Though in most applications they all preform the same. You can look at the model's accuracy, but that is not always the best in that the most accurate model me be overfitted and not generalizable. It is hard to provide more information than that without seeing the results.