Random Forest (RF) model outperforms the K-Nearest Neighbors (KNN) model in terms of predictive accuracy and overall performance, proved by Figure that demonstrates a greater concordance between predicted and actual values, particularly for higher values, which the KNN model fails to accurately estimate. This suggests that RF makes more effective decisions in capturing trends within the data, likely due to its ability to model non-linear interactions between variables.