Why is DRF (Distributed Random Forest) slower than XGBoost in h2o automl

Question

I am using H2O Auto ML and while comparing the time for XGBoost and D-RandomForest, DRF is taking a lot more time than XGBoost!

Code for DRF:

x = hf.columns
y = target_metric
x.remove(y)
aml = H2OAutoML(max_models=1, seed=1, include_algos=["DRF"])
aml.train(x=x, y=y, training_frame=hf)
m = aml.leader
varimp = m.varimp(use_pandas=True)

Code for XGboost:

x = hf.columns
y = target_metric
x.remove(y)
aml = H2OAutoML(max_models=1, seed=1, include_algos=["XGBoost"])
aml.train(x=x, y=y, training_frame=hf)
m = aml.leader
varimp = m.varimp(use_pandas=True)

H2O-AUTOML