There may exist many possible models to solve a given problem at hand. Based on your modeling decision there are usually two different ways to complete the machine learning lifecycle.
1st scenario. Training a single model with a training dataset and final evaluation with the test set.
2nd scenario. Training multiple models with training/validation dataset and final evaluation with the test set.
In case of (1st scenario), you will follow the following approach:
Divide the data into training and test sets. (Usually 70/30 splits)
Select your preferable model.
Train it with a training dataset.
Assess the trained model in the test set. (no need to perform validation in your trained model)
In case of (2nd scenario), you will follow the following approach:
Divide the data into training, validation, and test sets. (Usually 50/25/25 splits)
Select the initial model/architecture.
Train the model with a training dataset.
Evaluate the model using the validation dataset.
Repeat steps (b) through (d) for different models or training parameters.
Select the best model based on evaluation and train the best model with combined (training + validation) datasets.
Assess the trained model in the test set.