The correct approach is to incorporate conditional hyperparameters that are appropriate for subsets of scenarios. This method allows for a more tailored and efficient search space by focusing on relevant hyperparameters based on the values of others. For example, using Hyperopt's hp.choice function, you can define a search space where certain hyperparameters are only included under specific conditions, such as a learning rate for linear models or max depth for tree-based models. This strategy not only enhances the efficiency of the hyperparameter optimization process but also accommodates a wider range of scenarios within a single optimization framework, leading to better model performance and faster convergence.