The VC dimension serves as a powerful tool for understanding the learnability of hypothesis classes in statistical learning theory. When you consider a hypothesis class, its VC dimension gives you a concrete way to judge whether the class is "too large" or "too small" for a learning algorithm to generalize well from finite data. Specifically, a finite VC dimension is both a necessary and sufficient condition for a hypothesis class to be probably approximately correct (PAC) learnable under certain conditions. This means that if a hypothesis class has a finite VC dimension, you can guarantee — with enough data — that empirical risk minimization will lead to low generalization error. Conversely, if the VC dimension is infinite, no matter how much data you collect, there is always the possibility for the class to overfit, making reliable learning impossible.

For instance, as you saw in earlier examples, the class of intervals on the real line has a VC dimension of $2$, which means it can shatter any set of two points but not three. This finite VC dimension ensures that, given enough labeled examples, you can learn an interval that generalizes well to new data. On the other hand, if you consider the class of all possible subsets of the real line, the VC dimension is infinite, so no amount of data can guarantee good generalization. The VC dimension thus provides a clear criterion: if the VC dimension is finite, the class is learnable in the PAC sense; if it is infinite, it is not.

However, while the VC dimension is a foundational concept, it is not the only factor that determines how well a hypothesis class will generalize in practice.