Another way of preventing overfitting is regularization. Recall that the unnecessary complexity of the model is a source of overfitting. Regularization adds extra parameters to the error function we're trying to minimize, in order to penalize complex models.
According to the principle of Occam's Razor, simpler methods are to be favored. William Occam was a monk and philosopher who, in around the year 1320, came up with the idea that the simplest hypothesis that fits data should be preferred. One justification is that we can invent fewer simple models than complex models. For instance, intuitively, we know that there are more high-polynomial models than linear ones. The reason is that a line (y=ax