diff --git a/supervised/automl.py b/supervised/automl.py
index 9d81569b..40bddca6 100644
--- a/supervised/automl.py
+++ b/supervised/automl.py
@@ -59,7 +59,7 @@ def __init__(
             total_time_limit (int): The total time limit in seconds for AutoML training.
                 It is not used when `model_time_limit` is not `None`.
 
-            mode (str): Can be {`Explain`, `Perform`, `Compete`}. This parameter defines the goal of AutoML and how intensive the AutoML search will be.
+            mode (str): Can be {`Explain`, `Perform`, `Compete`, `Optuna`}. This parameter defines the goal of AutoML and how intensive the AutoML search will be.
 
                 - `Explain` : To to be used when the user wants to explain and understand the data.
                     - Uses 75%/25% train/test split.
@@ -70,10 +70,15 @@ def __init__(
                     - Uses the following models: `Linear`, `Random Forest`, `LightGBM`, `XGBoost`, `CatBoost`, `Neural Network`, and `Ensemble`.
                     - Has learning curves and importance plots in reports.
                 - `Compete` : To be used for machine learning competitions (maximum performance).
-                    - Uses 10-fold CV (Cross-Validation).
-                    - Uses the following models: `Decision Tree`, `Random Forest`, `Extra Trees`, `XGBoost`, `CatBoost`, `Neural Network`,
+                    - Uses 80/20 train/test split, or 5-fold CV, or 10-fold CV (Cross-Validation) - it depends on `total_time_limit`. If not set directly, AutoML will select validation automatically.
+                    - Uses the following models: `Decision Tree`, `Random Forest`, `Extra Trees`, `LightGBM`,  `XGBoost`, `CatBoost`, `Neural Network`,
                         `Nearest Neighbors`, `Ensemble`, and `Stacking`.
                     - It has only learning curves in the reports.
+                - `Optuna` : To be used for creating highly-tuned machine learning models.
+                    - Uses 10-fold CV (Cross-Validation).
+                    - It tunes with Optuna the following algorithms: `Random Forest`, `Extra Trees`, `LightGBM`, `XGBoost`, `CatBoost`, `Neural Network`.
+                    - It applies `Ensemble` and `Stacking` for trained models.
+                    - It has only learning curves in the reports.               
 
             ml_task (str): Can be {"auto", "binary_classification", "multiclass_classification", "regression"}.