Random Forest Classifier - KFold CV Tunes Very Deep Trees --> Overfitting?
There are signs of overfitting the data when the model's performance drops on the test set considerably. 

Good feature engineering can also improves your model's performance. Considering that you have an imbalanced data set, you can look at resampling techniques for your data set such as random over/under-sampling and SMOTE. You can also extract feature importances from ensemble learners or regularized regression models like LASSO to see which features are contributing the most weight in your model's predictions. You could also apply various statistical tests such as ANOVA or chi-square square tests to gain further insights of your data. There is lots to do here. 

You can also reconsider using the ROC AUC metric and choose something better tuned to a class imbalance in your data set. The ROC curve plots the True Positive Rates (TPR) on the x-axis and the False Positive Rates(FPR) on the y-axis whose formulas respectively correspond to $$TPR=\frac{TP}{TP+FN}$$ and $$FPR=\frac{FP}{FP+TP}$$ Since TPR is based only on the TP instances, the ROC curve won't measure the effects of negative instances. The AUC does does not consider the class imbalance when evaluating overall model performance, so we cannot trust it to weigh on the underrepresented class. 

One way to resolve this is by using the Matthews Correlation Coefficient since it takes into account true and false positives and negatives. This helps represent the minority class a lot more during your model evaluation.

You could also look into random grid searched for your hyperparameter tuning; this method uses a fixed number of parameter settings sampled from your specified distribution. This allows you to get a general neighborhood of your parameter space where you can use a brute force grid search in a shorter parameter space. Then you can use cross validation to evaluate your model as you normally would.

This is just a handful of things to consider, there are many other things that you may find are suitable for your problem.