Access Point (AP) selection plays a critical role in optimizing the performance of cell-free networks, which have emerged as a promising paradigm to enhance spectral efficiency, energy efficiency, and user fairness. Unlike traditional cellular networks, where users are associated with a single base station, cell-free networks enable dynamic AP selection based on channel conditions, network load, and user requirements. This survey provides a comprehensive review of AP selection strategies in cell-free networks, categorizing them into fixed and dynamic approaches, centralized and distributed schemes, and model-based and learning-based methods. We discuss optimization-based techniques, heuristic methods, machine learning-based approaches, and game-theoretic solutions for AP selection. Furthermore, we analyze key performance metrics such as spectral efficiency, energy efficiency, and computational complexity. Finally, we highlight open challenges and future research directions, including scalability, real-time adaptability, and integration with 6G and AI-driven network architectures. This survey aims to serve as a valuable reference for researchers and engineers working on next-generation wireless communication systems.