diff --git a/hlink/linking/model_exploration/link_step_train_test_models.py b/hlink/linking/model_exploration/link_step_train_test_models.py
index 8e391b8..a5e0273 100644
--- a/hlink/linking/model_exploration/link_step_train_test_models.py
+++ b/hlink/linking/model_exploration/link_step_train_test_models.py
@@ -60,6 +60,7 @@ def _run(self) -> None:
             .cache()
         )
 
+        # Stores suspicious data
         otd_data = self._create_otd_data(id_a, id_b)
 
         n_training_iterations = config[training_conf].get("n_training_iterations", 10)
@@ -101,6 +102,9 @@ def _run(self) -> None:
             for i in range(len(threshold_matrix)):
                 results_dfs[i] = _create_results_df()
 
+            # Collect auc values so we can pull out the highest
+            splits_results = []
+
             first = True
             for split_index, (training_data, test_data) in enumerate(splits, 1):
                 split_start_info = f"Training and testing the model on train-test split {split_index} of {n_training_iterations}"
@@ -140,6 +144,13 @@ def _run(self) -> None:
 
                 pr_auc = auc(recall, precision)
                 print(f"The area under the precision-recall curve is {pr_auc}")
+                splits_results.append(
+                    {
+                        "auc": pr_auc,
+                        "predictions_tmp": predictions_tmp,
+                        "predict_train_tmp": predict_train_tmp,
+                    }
+                )
 
                 if first:
                     prc = pd.DataFrame(
@@ -159,45 +170,54 @@ def _run(self) -> None:
 
                     first = False
 
-                i = 0
-                for threshold_index, (alpha_threshold, threshold_ratio) in enumerate(
-                    threshold_matrix, 1
-                ):
-                    logger.debug(
-                        f"Predicting with threshold matrix entry {threshold_index} of {len(threshold_matrix)}: "
-                        f"{alpha_threshold=} and {threshold_ratio=}"
-                    )
-                    predictions = threshold_core.predict_using_thresholds(
-                        predictions_tmp,
-                        alpha_threshold,
-                        threshold_ratio,
-                        config[training_conf],
-                        config["id_column"],
-                    )
-                    predict_train = threshold_core.predict_using_thresholds(
-                        predict_train_tmp,
-                        alpha_threshold,
-                        threshold_ratio,
-                        config[training_conf],
-                        config["id_column"],
-                    )
-
-                    results_dfs[i] = self._capture_results(
-                        predictions,
-                        predict_train,
-                        dep_var,
-                        model,
-                        results_dfs[i],
-                        otd_data,
-                        alpha_threshold,
-                        threshold_ratio,
-                        pr_auc,
-                    )
-                    i += 1
-
                 training_data.unpersist()
                 test_data.unpersist()
 
+            # pluck out predictions_tmp, predict_train_tmp associated with highest pr_auc
+            best_pr_auc = 0.0
+            best_predictions_tmp = None
+            best_predict_train_tmp = None
+            for a in splits_results:
+                if a["auc"] > best_pr_auc:
+                    best_prediction_tmp = a["predictions_tmp"]
+                    best_predict_train_tmp = a["predict_train_tmp"]
+
+            i = 0
+            for threshold_index, (alpha_threshold, threshold_ratio) in enumerate(
+                threshold_matrix, 1
+            ):
+                logger.debug(
+                    f"Predicting with threshold matrix entry {threshold_index} of {len(threshold_matrix)}: "
+                    f"{alpha_threshold=} and {threshold_ratio=}"
+                )
+                predictions = threshold_core.predict_using_thresholds(
+                    best_predictions_tmp,
+                    alpha_threshold,
+                    threshold_ratio,
+                    config[training_conf],
+                    config["id_column"],
+                )
+                predict_train = threshold_core.predict_using_thresholds(
+                    best_predict_train_tmp,
+                    alpha_threshold,
+                    threshold_ratio,
+                    config[training_conf],
+                    config["id_column"],
+                )
+
+                results_dfs[i] = self._capture_results(
+                    predictions,
+                    predict_train,
+                    dep_var,
+                    model,
+                    results_dfs[i],
+                    otd_data,
+                    alpha_threshold,
+                    threshold_ratio,
+                    best_pr_auc,
+                )
+                i += 1
+
             for i in range(len(threshold_matrix)):
                 desc_df = _append_results(desc_df, results_dfs[i], model_type, params)