The Vapnik-Chervonenkis Dimension (VC Dimension) is a concept in statistical learning theory that measures the capacity of a set of functions to classify data points. It indicates the largest number of points that can be arranged in any possible way without error by a given hypothesis class. A higher VC Dimension suggests a more complex model that can fit a wider variety of data patterns. Understanding VC Dimension helps in assessing 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 this is crucial for effective machine learning.