How does the fitnet train a network in MATLAB?

I trained a using the fitnet option through nftool on MATLAB R2016a, but I having a hard time understanding how actually the train works. I used the following code:

        % Solve an Input-Output Fitting problem with a Neural Network
        % Script generated by Neural Fitting app
        % Created 20-Sep-2017 16:16:49
        %
        % This script assumes these variables are defined:
        %
        %   Input_train - input data.
        %   Target_train - target data.

        x = Input_train;
        t = Target_train;

        % Choose a Training Function
        % For a list of all training functions type: help nntrain
        % 'trainlm' is usually fastest.
        % 'trainbr' takes longer but may be better for challenging problems.
        % 'trainscg' uses less memory. Suitable in low memory situations.
        trainFcn = 'trainlm';  % Levenberg-Marquardt backpropagation.

        % Create a Fitting Network
        hiddenLayerSize1 = 12;
        hiddenLayerSize2 = 12;
        net = fitnet([hiddenLayerSize1,hiddenLayerSize2],trainFcn);
        net.trainParam.epochs=5000; %more epochs

        % Choose Input and Output Pre/Post-Processing Functions
        % For a list of all processing functions type: help nnprocess
        net.input.processFcns = {'removeconstantrows','mapminmax'};
        net.output.processFcns = {'removeconstantrows','mapminmax'};

        % Setup Division of Data for Training, Validation, Testing
        % For a list of all data division functions type: help nndivide
        net.divideFcn = 'dividerand';  % Divide data randomly
        net.divideMode = 'sample';  % Divide up every sample
        net.divideParam.trainRatio = 80/100;
        net.divideParam.valRatio = 10/100;
        net.divideParam.testRatio = 10/100;

        % Choose a Performance Function
        % For a list of all performance functions type: help nnperformance
        net.performFcn = 'mse';  % Mean Squared Error

        %Training Parameters
        %net.trainParam.show=1;  %# of ephocs in display
        %net.trainParam.epochs=10000;  %max epochs

        % Choose Plot Functions
        % For a list of all plot functions type: help nnplot
        net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
            'plotregression', 'plotfit'};

        % Train the Network
        [net,tr] = train(net,x,t);

        % Test the Network
        y = net(x);
        e = gsubtract(t,y);
        performance = perform(net,t,y)

        % Recalculate Training, Validation and Test Performance
        trainTargets = t .* tr.trainMask{1};
        valTargets = t .* tr.valMask{1};
        testTargets = t .* tr.testMask{1};
        trainPerformance = perform(net,trainTargets,y)
        valPerformance = perform(net,valTargets,y)
        testPerformance = perform(net,testTargets,y)

        % View the Network
        view(net)

        % Plots
        % Uncomment these lines to enable various plots.
        figure, plotperform(tr)
        figure, plottrainstate(tr)
        figure, ploterrhist(e)
        figure, plotregression(t,y)
        %figure, plotfit(net,x,t)

        % Deployment
        % Change the (false) values to (true) to enable the following code blocks.
        % See the help for each generation function for more information.
        if (false)
            % Generate MATLAB function for neural network for application
            % deployment in MATLAB scripts or with MATLAB Compiler and Builder
            % tools, or simply to examine the calculations your trained neural
            % network performs.
            genFunction(net,'myNeuralNetworkFunction');
            y = myNeuralNetworkFunction(x);
        end
        if (false)
            % Generate a matrix-only MATLAB function for neural network code
            % generation with MATLAB Coder tools.
            genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes');
            y = myNeuralNetworkFunction(x);
        end
        if (false)
            % Generate a Simulink diagram for simulation or deployment with.
            % Simulink Coder tools.
            gensim(net);
        end

I split the data 80% for training, 10% for test and 10% for validation. After the training ends, it outputs on the workplace calculated targets for each one of these blocks and also a single 'y' matrix with targets for the whole data set.

Does it means that the network generalize the targets for all my inputs during training? Also, what is the purpose of the validation block?

Thanks in advance!