Tuning hyperparameters for sequential/iterative models is challenging because each iteration depends on the results of the previous one, making parallelization difficult. Examples of such models include gradient-based optimization algorithms and Markov chain Monte Carlo methods. These models require careful handling to ensure that the iterative process is not compromised by parallelization attempts.