Vapnik-Chervonenkis theory, often abbreviated as VC theory, is a framework in statistical learning theory that helps understand the capacity of a statistical classification algorithm. It focuses on the concept of shattering, which refers to the ability of a model to perfectly classify any arrangement of data points. The more complex a model is, the more data it can shatter, but this can also lead to overfitting. The theory provides a way to measure the generalization ability of a model, which is its performance on unseen data. By analyzing the VC dimension, researchers can determine how well a model will perform in practice, balancing complexity and accuracy to avoid overfitting while ensuring effective learning.