It is possible to use GridSearchCV to tune hyperparameters for multiple models simultaneously. This approach is called "grid searching over multiple models," and it involves creating a pipeline that combines multiple models with different hyperparameters, and then using GridSearchCV to search over the pipeline’s hyperparameters.
If you want to learn more about GridSearchCV, you can check out this thread
Using parallel processing in scikit learn to speed up GridSearchCV
Here is an example of how to use GridSearchCV to tune hyperparameters for multiple models simultaneously
This code performs hyperparameter tuning using GridSearchCV on a pipeline that combines two models, SVC and RandomForestClassifier, on the Iris dataset. The hyperparameters for each model are defined in a dictionary, and the ‘accuracy’ scoring metric is used to evaluate the models. The best hyperparameters for each model are identified by fitting the pipeline to the data using cross-validation.