During the last years, the exponential growth of machine learning and deep learning applications has remarked the need for efficient and scalable model selection strategies. Traditional model selection approaches often rely on extensive offline evaluation by means of predefined performance metrics. However, modern real-world applications typically involve non-stationary or streaming data, making it extremely complex to select the best model a priori. Online model selection has emerged as a crucial paradigm that enables real-time evaluation and adaptation of model selection based on incoming data streams. Industrial examples include autonomous systems such as robotics or self-driving cars, whose tasks can involve sensor fusion, real-time path planning and obstacle avoidance.