Random Forest:

Random Forest shows high precision, recall, accuracy, and F$1-$Score. It seems to perform well across the board. Consider using this model as a strong baseline or benchmark.

Decision Tree:

Decision Tree also exhibits excellent performance, particularly with high precision, recall, accuracy, and F$1-$Score. It can be a good choice, especially if interpretability is a priority.

Gradient Boosting:

Gradient Boosting has high precision and recall, but slightly lower accuracy compared to Random Forest. It might be worth considering, especially if achieving a balance between precision and recall is crucial for your application.

Multilayer Perceptron $($Neural Network$)$:

The Multilayer Perceptron shows competitive performance, with good precision, recall, accuracy, and F$1-$Score. If you are interested in exploring neural network models, this could be a viable option.

One$-$Vs$.-$Rest:

One$-$Vs$.-$Rest provides balanced performance with decent precision, recall, accuracy, and F$1-$Score. It can be considered as an alternative, especially if you seek a simpler model.

Support Vector Machine $($SVM$)$:

SVM has a balanced performance, but precision and recall are comparatively lower than some other models. Consider this model if interpretability and simplicity are important, as SVMs can be effective in high$-$dimensional spaces.

Logistic Regression:

Logistic Regression has good precision, recall, accuracy, and F$1-$Score. It's a simple and interpretable model, suitable as a baseline or in scenarios where model complexity needs to be minimized.

Naive Bayes:

Naive Bayes shows high recall but lower precision. Depending on your specific use case and the importance of precision, this model may or may not be suitable.