The VC dimension, or Vapnik-Chervonenkis dimension, is a measure of the capacity of a statistical classification model. It quantifies the model's ability to classify points in various ways, specifically how many points can be shattered, or perfectly classified, by the model. A higher VC dimension indicates a more complex model that can fit a wider variety of data patterns. In practical terms, the VC dimension helps in understanding the trade-off between model complexity and generalization. A model with a high VC dimension may fit training data well but could overfit, leading to poor performance on unseen data. Balancing complexity and generalization is crucial for effective machine learning.