This text discusses the importance of optimizing Large Language Models (LLMs) with cross-validation techniques to ensure reliable and generalizable performance. It highlights the challenges posed by LLMs' massive capacity, memorization risks, distribution shifts, and data leakage concerns. The article presents four comprehensive cross-validation techniques for optimizing LLMs: k-fold cross-validation, time-series cross-validation, rolling-origin cross-validation, and group k-fold cross-validation. These methods are implemented using popular deep learning frameworks like PyTorch, Hugging Face Transformers, and Optuna. The text emphasizes the need for careful data splitting, domain-specific benchmarking, and continuous monitoring of model performance to build robust LLMs. It also introduces Galileo, an end-to-end solution that connects experimental evaluation with production-ready AI systems, providing a comprehensive approach to cross-validation for LLMs.