This is true to the extent that you are not overfitting your dataset. Neural networks and random trees are quite good at fitting anything! And still they can perform poorly on your validation set.
Not only. Your example is too particular. I would say that overfitting tends to occur when one do not understand the underlying dynamic of a system you are trying to model. Any model with enough degrees of liberties can fit anything and still explain nothing.