The Vapnik-Chervonenkis dimension (VC dimension) is a measure of the capacity of a statistical classification model. It quantifies the model's ability to classify points in various ways, indicating how complex the model is. A higher VC dimension means the model can fit more complex patterns in data, but it may also lead to overfitting. In simple terms, the VC dimension helps determine how well a model can generalize from training data to unseen data. It is a crucial concept in statistical learning theory, developed by Vladimir Vapnik and Alexey Chervonenkis, to understand the trade-off between model complexity and performance.