SKLearn VotingRegressor - why so slow?

Question

I'm trying to work with SciKit-Learn's VotingRegressor, but I find the experience quite frustrating due to the apparent overhead this class adds.

All it should be doing according to the documentation is

...fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction.

But by doing this, I find it somehow increases the runtime by LOADS. Why?

For example, if I import 6 different regressors and train them individually, it amounts to around 5 minutes of training on my computer. Based on the description, the only additional step the VotingRegressor takes is it averages each predictor's prediction. However, when I pass the same 6 regressors to a VotingRegressor and start training, the training keeps running well above the 20 minute mark.

For getting an average, I wouldn't expect an over 5-fold increase in runtime (I'm currently running a training with over 30 minutes passed and still not stopped). What is the overhead that VotingRegressor is adding? Keep in mind this is happening with a circa 30 000 x 150 sized dataset.