Model complexity refers to the intricacy of a mathematical model used to represent data. It can be influenced by the number of parameters, the structure of the model, and the relationships it captures. A more complex model can fit the training data better but may also lead to overfitting, where the model performs poorly on new, unseen data. Balancing model complexity is crucial in fields like machine learning and statistics. A simpler model may generalize better, while a complex model might capture more nuances in the data. The goal is to find an optimal level of complexity that maximizes predictive performance without sacrificing generalization.