The Vapnik-Chervonenkis Theory, often abbreviated as VC Theory, is a fundamental concept in statistical learning theory. It helps us understand the capacity of a statistical model to learn from data. Specifically, it measures how well a model can classify different sets of points in a space, indicating its ability to generalize from training data to unseen data. One of the key ideas in VC Theory is the concept of VC dimension, which quantifies the complexity of a model. A higher VC dimension means the model can fit more complex patterns, but it also risks overfitting, where the model performs well on training data but poorly on new data.