RF and DT overfitting
Yes, and that benchmark is called validation data. The idea is to split your data in some training data, which is what you are going to fit to your models in order to estimate the parameters in them (and make them learn), and some validation data, which is the data you are going to use in order to evaluate your model. 

What you can do is compute the error on your validation data (which the models haven't been trained on) for the different models you want to use, and you would like to keep the model with the lowest validation error.