Using the same hyperparameters for the models in an ensemble is usually not the best idea.
This might lead to correlated outputs of the models and thus a low model variety.
I think this toy example was used in Stanford’s CS231n, but I cannot find the source anymore.
We compare an ensemble of good but correlated and worse but uncorrelated models:
target - 1, 1, 1, 1, 1, 1, 1, 1, 1, 1

case 1:
modelA - 1, 1, 1, 1, 1, 1, 1, 1, 0, 0 - 0.8
modelB - 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 - 0.9
modelC - 1, 1, 1, 1, 1, 1, 1, 1, 0, 0 - 0.8
-------------------------------------------
majority - 1, 1, 1, 1, 1, 1, 1, 1, 0, 0 - 0.8

case 2:
modelA - 1, 0, 1, 1, 1, 1, 1, 0, 0, 1 - 0.7
modelB - 0, 1, 1, 1, 1, 1, 0, 1, 0, 1 - 0.7
modelC - 1, 1, 1, 0, 1, 1, 1, 1, 1, 0 - 0.8
-------------------------------------------
majority - 1, 1, 1, 1, 1, 1, 1, 1, 0, 1 - 0.9

As you can see this is a toy example, but even though we ensemble worse models, the benefit comes from uncorrelated outputs, i.e. a high model variety.