Yes, absolutely, for any size of the dataset and model this is strictly necessary. You can use cross-validation, Leave-One-Out CV, or bootstrap techniques (e.g. 0.632+ bootstrap). You don't need to validate if you don't have any hyperparameters, but this is very rarely the case; the only examples I can think of is Random Forest and Extremely Randomized Trees, where sufficiently large number of trees is typically enough.